Artificial Intelligence / Machine Learning

The feats achieved through AI and machine learning are astonishing and can feel like modern wizardry. But without clear ethical reasoning and principled leadership, this utopian promise could tumble all too quickly into a dystopian nightmare.

While travelers are sprinting to tight connecting flights, airports are scrambling to make predictions. Visiting Associate Professor Yael Grushka-Cockayne, alongside Heathrow Airport and researchers at University College London, recently built and demoed a machine learning model that removes the guesswork.

Is Tuesday actually the best time to book a flight? Emily Batt (MS/MBA '20), formerly senior product manager at KAYAK, debunks this urban myth and gives us a peek inside the company’s price prediction models.

Even a perfectly designed algorithm makes decisions based on inputs from an imperfect world. Harvard Magazine pulls back the curtain on how AI operates — outside a vacuum, in real life — and the ethical awareness needed so the world being built is one we actually want to live in.

Despite the number of high-stakes applications, Al doesn’t come with a warning label. The health-tech professionals behind this article recommend concepts and tools from clinical research to serve as a starting point in navigating the complicated territory that is AI regulation.

How do we use AI technologies to address bigger social issues? What new regulatory and governance models are needed? From our 2018 Future Assembly, Harvard Law School Professor Chris Bavitz starts the multidisciplinary conversation.

Diversity of thought isn’t a nice-to-have when it comes to tech and business; it’s a requirement. Our 2019 Digital Transformation Summit explored the ethics of AI and implications for business decision-makers — from the perspectives of a philosopher, general counsel, and CEO.

Technologists, managers, and policymakers all have a seat at the table here. Assembly, a collaboration of the Berkman Klein Center for Internet & Society at Harvard and the MIT Media Lab, is about creating space for cross-sector teams to crack the code to some of AI’s toughest ethical and governance problems — problems that would otherwise fall out of reach.

Fairness has always been something we've struggled with as a society. David Weinberger, a senior researcher at the Berkman Klein Center for Internet & Society at Harvard, unpacks why fairness is a problematic framing of the ethical issues machine learning forces us to confront.

Globally, one-third of cervical cancer deaths take place in India — a country that suffers from a low patient-to-pathologist ratio. Tech startup, Aindra Systems is tackling this disparity through artificial intelligence. With automation comes quicker diagnoses and the opportunity to democratize access to quality care.

Machine learning is a popular topic in most industries these days, and it will come as no surprise that this is true for astronomy and astrophysics as well. University of Colorado PhD student Avery Schiff explores how a deep learning algorithm is being used to classify galaxy morphologies in this article from graduate student astronomy journal Astrobites.

Manuela is the Head of J.P. Morgan AI Research and is on leave from Carnegie Mellon University as the Herbert A. Simon University Professor in the School of Computer Science, and the past head of the Machine Learning Department.